Real time change point detection by incremental PCA in large scale sensor data

Dmitry Mishin, Kieran Brantner-Magee, Ferenc Czako, Alexander S. Szalay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The article describes our work with the deployment of a 600-piece temperature sensor network, data harvesting framework, and real time analysis system in a Data Center (hereinafter DC) at the Johns Hopkins University. Sensor data streams were processed by robust incremental PCA and K-means clustering algorithms to identify outlier and changepoint events. The output of the signal processing system allows us to better understand the temperature patterns of the DataCenter's inner space and make possible the online detection of unusual transient and changepoint events, thus preventing hardware breakdown, optimizing the temperature control efficiency, and monitoring hardware workloads.

Original languageEnglish (US)
Title of host publication2014 IEEE High Performance Extreme Computing Conference, HPEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479962334
DOIs
StatePublished - Feb 11 2014
Event2014 IEEE High Performance Extreme Computing Conference, HPEC 2014 - Waltham, United States
Duration: Sep 9 2014Sep 11 2014

Publication series

Name2014 IEEE High Performance Extreme Computing Conference, HPEC 2014

Other

Other2014 IEEE High Performance Extreme Computing Conference, HPEC 2014
Country/TerritoryUnited States
CityWaltham
Period9/9/149/11/14

ASJC Scopus subject areas

  • Software

Fingerprint

Dive into the research topics of 'Real time change point detection by incremental PCA in large scale sensor data'. Together they form a unique fingerprint.

Cite this